Applications in Finance
From human to data-driven decision-making …
… where black boxes are recipe for disaster.
Ground Truthing
Probabilistic Models
Counterfactual Reasoning
CounterfactualExplanations.jl is a package for generating Counterfactual Explanations (CE) and Algorithmic Recourse (AR) for black-box algorithms. Both CE and AR are related tools for explainable artificial intelligence (XAI). While the package is written purely in Julia, it can be used to explain machine learning algorithms developed and trained in other popular programming languages like Python and R. See below for short introduction and other resources or dive straight into the docs.
TL;DR: We find that standard implementation of various SOTA approaches to AR can induce substantial domain and model shifts. We argue that these dynamics indicate that individual recourse generates hidden external costs and provide mitigation strategies.
In this work we investigate what happens if Algorithmic Recourse is actually implemented by a large number of individuals.
Figure 4 illustrates what we mean by Endogenous Macrodynamics in Algorithmic Recourse:
We argue that these shifts should be considered as an expected external cost of individual recourse and call for a paradigm shift from individual to collective recourse in these types of situations. We demonstrate empirically that these types of dynamics do in fact occur and introduce mitigation strategies.
LaplaceRedux.jl (formerly BayesLaplace.jl) is a small package that can be used for effortless Bayesian Deep Learning and Logistic Regression trough Laplace Approximation. It is inspired by this Python library and its companion paper.
ConformalPrediction.jlConformalPrediction.jl is a package for Uncertainty Quantification (UQ) through Conformal Prediction (CP) in Julia. It is designed to work with supervised models trained in MLJ (Blaom et al. 2020). Conformal Prediction is distribution-free, easy-to-understand, easy-to-use and model-agnostic.
Read on …
… or get involved! 🤗
CounterfactualExplanations.jlConformalPrediction.jlCounterfactual Reasoning and Probabilistic Methods for Trustworthy AI with Applications in Finance — Patrick Altmeyer